class_docstr: Deterministic/Degenerate distribution.
  Parameters
  ----------
  value
      Tensor containing the values, of shape `(*batch_shape, *event_shape)`.
  F
code: def quantile(self, level: Tensor) -> Tensor:
      F = self.F
      # we consider level to be an independent axis and so expand it
      # to shape (num_levels, 1, 1, ...)
  
      for _ in range(self.all_dim):
          level = level.expand_dims(axis=-1)
  
      quantiles = F.broadcast_mul(self.value, level.ones_like())
      level = F.broadcast_mul(quantiles.ones_like(), level)
  
      minus_inf = -quantiles.ones_like() / 0.0
      quantiles = F.where(
          F.broadcast_logical_or(
              level != 0, F.contrib.isnan(quantiles)
          ),
          quantiles,
          minus_inf,
      )
  
      nans = level.zeros_like() / 0.0
      quantiles = F.where(level != level, nans, quantiles)
  
      return quantiles
  
imports: from typing import Dict, List, Optional, Tuple, Type
  import mxnet
  import numpy
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .distribution import Distribution, _sample_multiple, getF
  from .distribution_output import DistributionOutput
label_desc: Use image node_id 11 for calling the Deterministic obj's underlying member method code with example usage: obj.quantile(level) and returns: quantiles